Kim Hyunji, Lim Sejin, Kang Yeajun, Kim Wonwoong, Kim Dukyoung, Yoon Seyoung, Seo Hwajeong
Department of Convergence Security, Hansung University, Seoul 02876, Republic of Korea.
Entropy (Basel). 2023 Jun 28;25(7):986. doi: 10.3390/e25070986.
With the development of artificial intelligence, deep-learning-based cryptanalysis has been actively studied. There are many cryptanalysis techniques. Among them, cryptanalysis was performed to recover the secret key used for cryptography encryption using known plaintext. In this paper, we propose a cryptanalysis method based on state-of-art deep learning technologies (e.g., residual connections and gated linear units) for lightweight block ciphers (e.g., S-DES, S-AES, and S-SPECK). The number of parameters required for training is significantly reduced by 93.16%, and the average of bit accuracy probability increased by about 5.3% compared with previous the-state-of-art work. In addition, cryptanalysis for S-AES and S-SPECK was possible with up to 12-bit and 6-bit keys, respectively. Through this experiment, we confirmed that the-state-of-art deep-learning-based key recovery techniques for modern cryptography algorithms with the full round and the full key are practically infeasible.
随着人工智能的发展，基于深度学习的密码分析受到了积极研究。有许多密码分析技术。其中，利用已知明文进行密码分析以恢复用于密码加密的密钥。在本文中，我们针对轻量级分组密码（如S-DES、S-AES和S-SPECK）提出了一种基于最新深度学习技术（如残差连接和门控线性单元）的密码分析方法。与之前的最新工作相比，训练所需的参数数量显著减少了93.16%，比特准确率概率平均提高了约5.3%。此外，分别使用多达12位和6位密钥对S-AES和S-SPECK进行密码分析成为可能。通过该实验，我们证实了针对具有完整轮数和完整密钥的现代密码算法，基于深度学习的最新密钥恢复技术在实际中是不可行的。
